IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 9 - Issue 6, June 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Analysis The Sentiments Of Amazon Reviews Dataset By Using Linear SVC And Voting Classifier

[Full Text]

 

AUTHOR(S)

Sandeep Singh Sikarwar, Dr.Nirupma Tiwari

 

KEYWORDS

Sentiment Analysis, Classification Techniques, Naïve Bayes, Voting, Linear SVC , NLP,Data mining, amazon product review dataset.

 

ABSTRACT

Any opinion of a person that can convey emotions, attitudes, or opinions is known as a sentiment. The data analyzes that are collected from media reports, consumer ratings, social network posts, or microblogging sites are classified as opinion mining research. Analysis of sentiment should be viewed as a way of evaluating people for particular incidents, labels, goods, or businesses. The amount of views exchanged by people in micro-logging sites often increases, which makes nostalgic interpretations more and more common today. All sentiments may be categorized as optimistic, negative, or neutral under three groups. The characteristics are derived from the document term matrix using a bi-gram modeling technique. The sentiments are categorized among positive and negative sentiments. In this analysis, the Python language is used to apply the classification algo for the data obtained. The detailed accomplishment of LinSVC demonstrates greater precision than other algos.

 

REFERENCES

[1] Gregory, Michelle L., et al. "User-directed sentiment analysis: Visualizing the affective content of documents." Proceedings of the Workshop on Sentiment and Subjectivity in Text. Association for Computational Linguistics, 2006.
[2] Pak, Alexander, and Patrick Paroubek. "Twitter as a Corpus for Sentiment Analysis and Opinion Mining." LREC. Vol. 10.
[3] R. Xia, C. Zong, and S. Li, "Ensemble of feature sets and classification
algorithms for sentiment classification," Information Sciences, vol. 181,
no. 6, pp. 1138-1152, 2011/03/15/ 2011.
[4] R. Sharma, S. Nigam, and R. Jain, "Opinion mining of movie reviews at
document level," arXiv preprint arXiv:1408.3829, 2014.
[5] R. Sharma, S. Nigam, and R. Jain, "Polarity detection at the sentence level,"
International Journal of Computer Applications, vol. 86, no. 11, 2014.
[6] Baccianella, Stefano, Andrea Esuli, and FabrizioSebastiani. "SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining." LREC. Vol. 10. 2010.
[7] Godbole, Namrata, ManjaSrinivasaiah, and Steven Skiena. "Large-Scale Sentiment Analysis for News and Blogs." ICWSM 7 (2007): 21.
[8] Annett, Michelle, and GrzegorzKondrak. "A comparison of sentiment analysis techniques: Polarizing movie blogs." Advances in artificial intelligence. Springer Berlin Heidelberg, 2008. 25-35.
[9] Pang, Bo, Lillian Lee, and ShivakumarVaithyanathan. "Thumbs up?: sentiment classification using machine learning techniques." Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 2002.
[10] Pang, Bo, and Lillian Lee. "A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts." Proceedings of the 42nd annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2004.
[11] C. Wu, L. Shen, and X. Wang, "A new method of using contextual
information to infer the semantic orientations of context-dependent
opinions," in Artificial Intelligence and Computational Intelligence,
2009. AICI'09. International Conference on, 2009, vol. 4: IEEE, pp.
274-278.
[12] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, "Lexiconbased methods for sentiment analysis," Computational linguistics, vol. 37, no. 2, pp. 267-307, 2011.
[13] T. Zagibalov and J. Carroll, "Unsupervised classification of sentiment
and objectivity in Chinese text," in Proceedings of the Third
International Joint Conference on Natural Language Processing:
Volume-I, 2008.
[14] A. Tripathy and S. K. Rath, "Classification of the sentiment of reviews
using supervised machine learning techniques," International Journal of
Rough Sets and Data Analysis (IJRSDA), vol. 4, no. 1, pp. 56-74, 2017.
[15] M. R. Saleh, M. T. Martín-Valdivia, A. Montejo-Ráez, and L. UreñaLópez, "Experiments with SVM to classify opinions in different
domains," Expert Systems with Applications, vol. 38, no. 12, pp. 14799-
14804, 2011